In this paper the statistical model used is analysis of variance (ANOVA). Analysis of Variance (ANOVA) is a commonly statistical method that are often used in order to analyze the result of the model related to real world system. According to Burke (2001), Analysis of variance ( ANOVA) is used to compare and analyze
Particularly, regression analysis, a statistical process to estimate the connection among dependent and independent variables. Accordingly, by using regression analysis the analyst can create the score that produced by those variables to predict what company needs like customer purchase behavior. The third and the last model is assumptions. Both data and statistics have assumptions to make a viewpoint and conclusion about the predictive data. Assumptions are holding the key to our predictive analytics results.
Quantitative research is a method to quantify data with a combination of deductive logic and empirical observations to define and identify what factors or variables in the population that influence an outcome. The data is collected by using structured data collection instruments such as content analysis, discourse analysis, questionnaires and surveys to produce a narrow-angle lens which is able to eliminate biasness. Quantitative research is an important research approach to test the hypotheses and statistical analysis is developed to support the hypotheses. (Park, 2012) It is often designs in closed-ended questions to gather and interpret the data from a large sample which will be the representative of the whole population. Qualitative research
In this essay I will be covering the statistical process control system. The statistical process control system, or SPC for short, is basically a quality control & improvement strategy information system. The statistical process control system is a data driven, graphic centered, process oriented operator run system designed to implement timely corrective action & also a way to identify quality problems & challenges. In this essay I will cover the main features & uses of the statistical process control system. There are several reasons of why many companies use the statistical process control system.
And methods like Agent-based modeling, Network Analysis, Scenario planning, Systems dynamics modeling. Also the tools like Causal loop diagrams (CLDs) , Innovation, Participatory Impact Pathways Analysis, Process mapping , Stock and flow diagrams , Systems archetypes. This is the huge collection of application that are involve in system thinking also if some people have same thinking “systems archetypes ” helps the people to understand the generic terms about the phenomena. Instead of using existing samples of system casual loop diagrams are created and investigated how things are related either in positive or negative way. Analysis and experiments are implemented to increase
Jeffreys wrote that Bayes’ theorem “is to the theory of probability what the Pythagorean theorem is to geometry.” Bayes' Theorem The particular formula from Bayesian probability we are going to use is called Bayes' Theorem, sometimes called Bayes' formula or Bayes' rule. This particular rule is most often used to calculate what is called the posterior probability. The posterior probability is the conditional probability of a future uncertain event that is based upon relevant evidence relating to it historically. In other words, if you gain new information or evidence and you need to update the probability of an event occurring, you can use Baye's Theorem to estimate this new probability. The formula is: P(A) is the probability of A occurring, and is called the prior probability.
For this research, Quantitative study was used as it involved deduction. Hypotheses was formulated; the dependent and independent variables were identified which assisted in measurements of objectives. Data was collected by using close-ended questionnaires. 5.1 Research paradigm According to the requirements of research question, it was decided that it would be appropriate to choose the positivism approach as the philosophical assumption and research paradigm for the study. Besides this, the quantitative approach was used during this research.
Typically, the highest a webpage is ranked in a search engine search results the more it will be visited by the search engine users (In fact, a search engine success mainly depends on whether it provides its users with the most relevant websites to their search queries-keywords). Search engines rank websites based on keywords that appear often and in main sections of a website (e.g title and headers) and which are relevant with the search engine users’ search queries. In order for the search engine’ s computer programs (spiders and crawlers) to identify and analyze the content of a website and its keywords, then the keywords must be converted to links. Links connect websites throughout the Internet and serve as content descriptions for search engines. How search engines assign value to the links is not in the scope of this report, as an extensive decomposition and technical analysis of a link’s individual elements should be made https://moz.com/beginners-guide-to-seo/growing-popularity-and-links .
2.0. The Qualitative versus Quantitative Research debate As previously shown, quantitative and qualitative methods stem from different ontologic, epistemologic and axiologic assumptions about the nature of research. Traditionally, quantitative methods are predominantly acknowledged within positivism whereas qualitative methods are dominant within the interpretivism or non-positivist studies (Bredillet, 2008). Generally speaking, quantitative methodology is concerned with attempts to quantify social phenomena and collect and analyze numerical data, and the use of statistical procedures to examine group means and variance (Ponterotto 2005; Tuli 2010). Qualitative methodology, on the other hand, is more concerned with understanding the meaning of
We can adopt a simple neural network as our forecasting system and select several technical indicators as input signals. After training the neural network we can test the validity of each individual indicator and its combinations. The experiments are then conducted on the time series data of a major stock index. Based on the results, we can find a more effective trading strategy to improve investment returns. To test the whole model, we should obtain the percentages of accurate predictions for different network topologies, different transfer functions, and different combinations of these basic technical indicators.